KiDS-SQuaD: The KiDS Strongly lensed Quasar Detection project

Abstract

New methods have recently been developed to search for strong gravitational lenses, in particular lensed quasars, in wide-field imaging surveys. Here, we compare the performance of three different, morphology-and photometry-based methods to find lens candidates within the Kilo-Degree Survey (KiDS) DR3 footprint (440 deg 2 ). The three methods are: i) a multiplet detection in KiDS-DR3 and/or Gaia-DR1, ii) direct modeling of KiDS cutouts and iii) positional offsets between different surveys (KiDS-vs-Gaia, Gaia-vs-2MASS), with purpose-built astrometric recalibrations. The first benchmark for the methods has been set by the recovery of known lenses. We are able to recover seven out of ten known lenses and pairs of quasars observed in the KiDS DR3 footprint, or eight out of ten with improved selection criteria and looser colour pre-selection. This success rate reflects the combination of all methods together, which, taken individually, performed significantly worse (four lenses each). One novelty of our analysis is that the comparison of the performances of the different methods has revealed the strengths and weaknesses of the approaches and, most of all, the complementarity. We finally provide a list of high-grade candidates found by one or more methods, awaiting spectroscopic follow-up for confirmation. Of these, KiDS 1042+0023 is, to our knowledge, the first confirmed lensed quasar from KiDS, exhibiting two quasar spectra at the same source redshift at either sides of a red galaxy, with uniform flux-ratio f ≈ 1.25 over the wavelength range 0.45µm < λ < 0.75µm.

LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks

“…In addition, the completed KiDS survey will cover an area of 1350 square degrees. We plan to apply our method to these completed KiDS data, together with that of Spiniello et al (2018), to find lensed quasars. Applying other complementary methods as Hartley et al (2017) SVM will aid in maximizing the exploration of the parameter space.…”

“…The size and homogeneity of this sample is required for the surveys primary science drivers, which include placing strong constraints on both the distribution of matter across cosmic time and the cosmological parameters of the universe through weak-lensing measurements; the subtle distortions introduced in galaxy shapes by cosmic shear (e.g., Hildebrandt et al 2017). At the same time, the combined power of the survey's superb image quality and wide area makes KiDS optimal for strong-lensing studies (Napolitano et al 2016;Petrillo et al 2017;Spiniello et al 2018). OmegaCAM has a one square degree field of view, with pixels that have an angular scale of 0.21 arcseconds, and KiDS will survey a total of ∼1350 square degrees in four optical bands (u, g, r and i) by the end of observations in 2019.…”

“…In terms of methodology and target selection our analysis differs from the work done by Spiniello et al (2018), who have focused their search exclusively on lensed quasar candidates in KiDS, by visual inspecting targets preselected using optical/infrared colours. Lens candidates have also been found in the KiDS DR3 data by Hartley et al (2017), who trained a Gabor-SVM finder.…”

Catalog of quasars from the Kilo-Degree Survey Data Release 3

“…The depth of KiDS, ∼ 25 mag in the r band (5σ), its multiwavelength ugri coverage, and availability of overlapping VIKING data at a similar depth (Edge et al 2013), make this survey an ideal resource for quasar science. This remains however a very much uncharted territory in KiDS, and only three studies so far presented QSO-related analyses based on this survey: Venemans et al (2015) focused on very high-redshift (z ∼ 6) quasars found in a combination of KiDS and VIKING data, Heintz et al (2018) studied a heavily reddened QSO identified in KiDS+VIKING, while Spiniello et al (2018) selected QSO-like objects over the KIDS DR3 footprint to search for strong-lensing systems.…”

Discovering gravitational lenses with artificial intelligence

“…Finally, Hartley et al (2017) have used a technique based on Support Vector Machines (SVMs) and applied it to KiDS. Instead Spiniello et al (2018) focused on the search of lensed quasars in KiDS using 3 different morphology based methods.…”